{"id":"W4412142028","doi":"10.1177/14759217251340416","title":"Deep learning-based 3D image reconstruction and damage mapping using neural radiance fields (Nerfacto)","year":2025,"lang":"en","type":"article","venue":"Structural Health Monitoring","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Research Manitoba","keywords":"Radiance; Artificial intelligence; Artificial neural network; Deep learning; Computer science; Computer vision; Image (mathematics); Remote sensing; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001537375,0.0003324588,0.0003658933,0.0002702667,0.0005969548,0.0001311983,0.0001464717,0.0001608232,0.00000958508],"category_scores_gemma":[0.00004640369,0.0003448034,0.00006170297,0.000357421,0.00008521588,0.000400945,0.00004904305,0.0008035302,0.000001341035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004202057,"about_ca_system_score_gemma":0.00006774892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001890097,"about_ca_topic_score_gemma":0.00001553813,"domain_scores_codex":[0.9982506,0.0000633786,0.0004528164,0.0003538093,0.0001552044,0.0007241854],"domain_scores_gemma":[0.9993652,0.0000718294,0.0001063105,0.0002270028,0.00007004205,0.0001596301],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003620114,0.000002201724,0.09786809,0.001369003,0.00006006192,0.00001857917,0.001078359,0.4771832,0.01362327,0.00004237025,0.00002272314,0.4086959],"study_design_scores_gemma":[0.0006388123,0.00004303217,0.1052111,0.0006350402,0.00001624177,0.00004150783,0.0007489836,0.8852205,0.00650558,0.0001949245,0.0003118026,0.0004324949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9752519,0.001465934,0.01568584,0.0001022782,0.006597457,0.0002502448,0.000002893668,0.0004024172,0.0002410123],"genre_scores_gemma":[0.9773748,0.0001219617,0.02159196,0.00004979439,0.0007703839,0.00001023432,0.000005527328,0.00004049787,0.00003485084],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4082634,"threshold_uncertainty_score":0.9999004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01134738243918965,"score_gpt":0.2720001858515111,"score_spread":0.2606528034123215,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}